w?s 23-7
POLICY RESEARCH WORKING PAPER 28 74
The Risks and Macroeconomic Impact
of HIV/AIDS in the Middle East
and North Africa
Why Waiting to Intervene Can Be Costly
David A. Robalino
Carol Jenkins
Karim El Maroufi
The World Bank
Middle East and North Africa Region -
Human Development Group
August 2002
I POLICY RESEARCH WORKING PAPER 2874
Abstract
Robalino, Jenkins, and El Maroufi develop a model of would be below 1 percent in 16 percent of the cases,
optimal growth to assess the risks of an HIV/AIDS while they would be above 3 percent in 50 percent of the
epidemic and the expected economic impact in nine cases. On average, GDP losses across countries for 2000-
countries in the Middle East and North Africa region- 2025 could approximate 35 percent of today's GDP. In
Algeria, Djibouti, Egypt, Iran, Jordan, Lebanon, all countries it is possible to observe scenarios where
Morocco, Tunisia, and Yemen. The model incorporates losses surpass today's GDP. The authors quantify the
an HIV/AIDS diffusion component based on two impact of expanding condom use and access to clean
transmission factors-sexual intercourse and exchange of needles for intravenous drug users. They show that these
infected needles among intravenous drug users. Given interventions act as an insurance policy that increases
high levels of uncertainty on the model parameters that social welfare. They also show that delaying action for
determine the dynamics of the epidemic and its five years can cost, on average, the equivalent of six
economic impact, the authors explore large regions of percentage points of today's GDP.
the parameter space. The prevalence rates in year 2015
This paper-a product of the Human Development Group, Middle East and North Africa Region-is part of a larger effort
in the region to raise awareness about the social and economic cost of HIV/AIDS. Copies of the paper are available free from
the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Cheikh Fall, room H8-118, telephone 202-
473-0632, fax 202-477-0036, email address cfalll@worldbank.org. Policy Research Working Papers are also posted on
the Web at http://econ.worldbank.org. David Robalino may be contacted at drobalino@worldbank.org. August 2002. (34
pages)
The Policy Research Working Paper Series disseminates the findiRzgs of eork in progress to encoafrage the excbange of ideas about
development issues. An objective of the series is to get the finidings ouit quickly, even if the presentations are less thanl fuilly polished. The
papers carry the names of the autbors and should be cited accordingly. The findings, interpretations, and concluisions expressed in this
paper are entirely those of the authors. They do not necessarily represenit the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Research Advisory Staff
Risks and Macro-Economic Impacts of HIV/AIDS in the
Middle East and North Africa:
Why waiting to intervene can be costly
by
David A. Robalino
drobalino@woldbank.org
Carol Jenkins
Karim El Maroufl
Introduction
In 1991, with the evidence available at the time, experts estimated that by the year 2000, 15-
20 million adults and 5-10 million children cumulatively would become infected with HIV. By the
end of 2001, The World Health Organization (WHO) reported 40 million people were living with
HIV/AIDS and 5 million new infections had occurred during the year. The great difference between
earlier projections and current estimates reflects both the unexpected spread of the virus and the
inadequacy of statistics used to track the epidemic. HIV does not always spread rapidly, though it
certainly can if conditions permit. Rapid social and economic changes underlie the virus's spread in
most of Africa, Russia, Central Asia, Eastern Europe, China, and elsewhere. HIV epidemics have
been particularly sensitive to large migrations of people, wars, economic downturns, and other drastic
alterations in social stability. WHO estimates that 1.2% of all adults on earth are presently infected
with HIV and all experts agree that the worst effects of the pandemic are yet to come.
This paper is a first attempt to evaluate the risks of an epidemic in 9 Middle East and North
Africa (MENA) countries' and its potential economic costs, while at the same time assessing the
welfare implications of two preventive interventions: expanding condom use and access to clean
needles for Injecting Drug Users (IDUs). WHO estimated that approximately 80,000 persons were
newly infected with HIV in the MENA region in the year 2001, and that about 0.2% of adults in the
region are currently infected. These figures are relatively low, compared to Africa, South and SE Asia
and the Caribbean, but do not assure safety for the region. Inadequate methods of surveillance,
universal in the region, can easily miss outbreaks in hidden populations. Further, even in low
prevalence nations, the situation can change rapidly, as has occurred in Indonesia and Nepal.
Unfortunately, in MENA countries, continued low levels of case detection through mostly
mandatory screening, the lack of appropriate behavioral data, and over-confidence in the protective
effects of social and cultural conservatism have dictated low priority for HIV/AIDS Yet, there is
evidence that the necessary risk factors to the spread of the epidemic are present. The first group is
comprised of those with known risky behaviors, such as prostitutes, their clients, IDUs, Men who
have Sex with Men (MSM), and those who acquire Sexually Transmitted Diseases (STDs). These
persons are immediately at-risk. While it is likely that they represent a minority in any country, our
analysis shows that any such group can.become the core of spread into the rest of the population,
depending upon the extent and nature of social linkages and networks. The next groups are those
who may be considered vulnerable (i.e., could be at-risk if and when their life situation changes). For
example, migrants going to work abroad, refugees, tourists traveling for fun and recreation, non-
injecting drug users who may switch to injecting when the availability or price of the drug changes,
and young people in general, in that some proportion will engage in non-marital sex under certain
conditions. Further, structural factors, such as poor and dysfunctional health care systems, raising
unemployment and poverty rates, and income and gender inequality create an environment that is
suitable for the diffusion of the epidemic (see Over, 2000; and Jenkins and Robalino, 2002, Ch. 4, for
econometric analysis of the socioeconomic determinants of IRV prevalence rates using cross-country
data).
Clearly, there are methodological difficulties to predict the dynamics of the epidemic in
different countries and to evaluate economic impacts. These difficulties are amplified by the lack of
substantial IRV-related social or behavioral research in the region. Our analysis builds upon the large
body of applied research on the economics of HIV/AIDS that has been conducted over the past 10
years. One tradition within this literature has focused on estimating econometrically, on the basis of
The countries included in the analysis are: Algeria, Djibouti, Egypt, Iran, Jordan, Lebanon, Morocco, Tunisia,
and Yemen.
-2 -
cross-country data, whether the HIV/AIDS incidence and prevalence rates impact economic growth.
Given the difficulties in isolating the effects of HIV/AIDS from other factors, the evidence is mixed.
Using an econometric model based on EPIMODEL (Chin, 1990), Bloom and Mahal (1997) find no
significant correlation between the HIV/AIDS incidence rate and economic growth. On the other
hand, using panel data on prevalence rates and GDP per capita, Bonnel (2000) finds that growth rates
in Africa's most affected countries could have been higher by 1 percentage point in the absence of
HTV/AIDS. A second tradition uses household surveys to study the impacts of the epidemic on
household income. Bollinger et al. (1999), for example, find that rural household in Kenya could see
their income fall by over 50% as a result of AIDS. In Kagar-Tanzania, Over et al. (1999) infer that
household food expenditure per capita after the death of a household member is reduced by 32% and
food consumption by 15%. A third tradition, to which our paper subscribes, uses simulation models
to explore the impacts of H1V/AIDS on the economy through three main channels: premature deaths
that affect the size and composition of the labor force, increases in morbidity and health expenditures
that affect total factor productivity, and potentially lower saving rates that affect investments and
growth. Micro-simulations by Soucat (2001) in the case of Chad and Ellis et al. (1997) in the case of
India, for example, predict that health expenditures could increase by a factor of 2 to 3. Macro-
simulation models, on the other hand, predict that in affected countries (countries where prevalence
rates have surpassed 5% and continue on an upward trend) reductions in per capita GDP growth rates
could be in the order of 0.5% to 1.5% per year (see, for instance, Over, 1992 for an application to
Sub-Saharan Africa; Cuddington, 1993 for an application to Tanzania; Leighton, 1993 for an
application to Thailand; Nalo and Aoko, 1994 for an application to Kenya; Lewis, 2000 and Quattek,
2000 for an application to South Africa; and MacFarlan and Sgherri, 2001 for a recent application to
Botswana). The structure of these models has evolved over time from the one sector Solow-type
model (Cuddington 1993), to models that allow to differentiate the effects that the epidemic has on
skilled, unskilled, and unemployed workers (see MacFarlan and Sgherri, 2001), and where the
savings rate of the economy is endogenous (see, Robalino et al., 2002 for an application to estimate
optimal reduction targets for the HIV/AIDS incidence rate in Kenya). General Equilibrium Models
have also been developed to evaluate the impact of the epidemic across sectors and relative prices
(see, Kambou et al., 1992 for an application to Cameroon). While these models necessarily leave out
important aspects of the interplay between the economy and the epidemic (e.g., the role of migration,
multi-sectorial heterogeneity, social costs related to the increase in single parents households and the
number of orphans, distortionary effects introduced when mobilizing resources to cure AIDS
patients), they are useful to put boundaries on the magnitude of the potential economic impacts of the
epidemic. The main limitation, however, is that model parameters rarely result from robust statistical
inference and standard sensitivity analysis leave wide regions of the parameter space unexplored.
The model used in this paper has many of the features of other macro-models in the
economics literature. The main addition is that it incorporates an H[V/AIDS diffusion component
where the virus transmission occurs through sexual intercourse and the exchange of infect needles
among IDUs. This allows us to simulate the impact of interventions such as improving access to
condoms and clean needles for IDUs. Nonetheless, our efforts have not concentrated on model
design, but rather on simulation design. Indeed, we recognize that the economy and the epidemic
create a complex system that is structurally unpredictable. This unpredictability deepens when data to
estimate model parameters is scant. The approach taken here has been to construct wide confidence
intervals for model parameters, based on estimates from the literature and known economic
constraints, and then conduct an extensive exploration of the parameter space to characterize the
ensemble of plausible futures that the model can generate given countries' initial conditions (see
Bankes, 1993; Lempert et al., 1996; and Robalino and Lempert, 2000, for other applications of
exploratory modeling methods). Finally, we analyze the robustness of policy interventions against
the wide range of futures.
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The paper is organized in 4 sections. Section 1 develops the model used in the analysis.
Section 2 applies the model to explore alternative diffusion scenarios and potential macroeconomic
impacts in MENA countries. Section 3 evaluates the welfare gains that could be induced by two
classic interventions: distributing condoms and improving access to clean needles for IDUs. Finally,
Section 4 summarizes the major conclusions from the analysis and discusses some of its limitations.
1. The Model
Our model focuses on 4 channels through which the epidemic affects the economy: the size
and composition of the labor force; productivity growth; health expenditures; and the savings rate of
the economy. Other channels, such as reductions in human capital resulting from an increase in the
number of orphans, who are less likely to fully develop their physical and intellectual capacities2, are
ignored for methodological reasons. Thus, it can be said that our model underestimates the economic
cost of the HIV/AIDS epidemic.
The model has been modified from its original version (see Robalino et al., 2002) in two
important ways. First, we introduce three types of labor than can be affected differently by the
epidemic: skilled labor, unskilled labor, and unemployed labor. Second, we couple the
macroeconomic model to an HIV/AIDS diffusion model that simultaneously takes into account
sexual transmission and transmission through the sharing of infected needles among IDUs (the two
major transmission mechanisms in the case of MENA countries). This is a convenient feature of the
model as it allows us to simulate the welfare implications of specific interventions such as increasing
condom use and access to clean needles for IDUs.
Another important feature of the model is that the savings rate of the economy is computed
endogenously to maximize inter-temporal social welfare. This enables us to simulate the economic
impacts of the epidemics under "ideal conditions" and to take a normative stance in terms of when
and how governments should intervene3. In fact, the assumption introduced in most models that
savings are reduced as a result of the epidemic is not supported by empirical data (see Robalino,
2002). In the absence of well functioning insurance markets, rational agents may actually increase
savings to protect themselves against potentially high future health expenditures (see Appendix A for
a technical discussion). The ultimate effect that HIV/AIDS has on the savings rate of the economy
will depend on how it affects agents' preferences (risk aversion, discount rates) and the govermnent
fiscal balance. For simplicity, our simulations will keep preferences constant and assume that
societies respond optimally to the shock resulting from the epidemic. This, again, would
underestimate the welfare impacts of the epidemic.
2 Research has shown that beyond the psychological impacts, among low-income populations, the death of one
parent is associated with a deterioration of nutritional status and lower school enrollment rates (see Ainsworth
M. and Koda G., 1993).
3 The implicit assumption is that the consumption of health services to treat AIDS patients is included in the
consumption bundle.
-4 -
Modeling the economy
The model is based on the assumption that the output of a given economy can be represented
as a simple function of human and produced capital.
Y, = L-K,At, (1)
where Y is GDP, K is the stock of produced capital, L is quality adjusted labor, our proxy for human
capital, and A is a scale factor. The growth rate of A captures changes in total factor productivity and
its dynamic is determined exogenously, we have:
log A, = log A,-, + r0 exp(-Sat), (2)
where ya is the growth rate of aggregate labor productivity and 8a is the yearly change in this growth
rate (see Pfizer, 1998 for a similar formulation).
Human capital is defined by:
L, =Ia,N,; is I, (3)
where N, is the number of workers of type i, a, is the productivity of labor type i, and I is the set of
labor types. It is convenient to rewrite equation (3) as:
L, = q,N, (4)
where q, is the average quality of labor at time t, given by:
qt = Y. a,Ni,; ie I, (5)
Nt,
For simplicity in this application we use three types of labor: skilled (a= l), unskilled (a=0.5),
and unemployed (a=O). Given little knowledge about the workings of the labor markets in the MENA
region, we have avoided modeling explicitly its dynamics (i.e., by formalizing labor supply and
demand decisions). Instead, we make assumptions about transition probabilities between different
types of labor and treat the growth rate of the aggregate labor force exogenously. This treatment is
sufficient to achieve our objective of been able to simulate the impact of alternative distributions of
the burden of FIV/AIDS across the labor force. Thus, we postulate:
N, = 1l [N, l - D,_ ]+ q2AWN, (6)
where N is a (3 ,1) vector giving the number of workers in each labor class, Tlj is a (3 ,3) matrix of
transition probabilities, D is a (3 ,1) vector giving the number of deaths due AIDS in each labor class,
Th is a (1 ,3) vector giving the share of new workers going to each labor class, and AN is the number
of new workers. The dynamics of the total number of workers is characterized as follows:
5-
Nt = exp[log(Nt, I) + r. exp(- int)]- AHt-10 (7)
{H =AN,
where H is the total number of HIV/AIDS infected individuals and fl is the aggregate HIV/AIDS
prevalence rate. The prevalence rate is determined by the HIV/AIDS diffusion model described in
the next section.
To derive the optimal savings rate, and therefore the dynamics of the stock of produced
capital, we assume that the economy operates along a 'path that maximizes social utility, assumed to
be a function of aggregate consumption. More specifically, we solve the following optimization
problem:
Max c: V(C,)=Ep'{N, (pt N, '}
s.t.: K, =K,-,(1-Jk)+(Yt-C,)t (8)
(1), (2), (4), and (5).
where S is the depreciation rate for produced capital, p is a discount factor, and X is the coefficient of
risk aversion. We use a standard constant elasticity of substitution utility function that is population
weighted. By aggregating across the population we are able to capture part of the welfare losses due
to the reduction of the labor force resulting from AIDS related deaths4.
It can be shown that under optimality conditions the dynamics of the stock of produced
capital can be approximated by:
'Aln(K /N,,1 ) = a, + a2(ln(K,, /N,+, ) - ln(T,)), (9)
where T, = q,A'('-°), and al and a2 are functions of the sequence {qt}, the growth rate of the labor
force, and of the parameters ya, k H, X, and p (see Appendix B).
Up to here, the HV/AIDS epidemic affects the size of the labor force and its composition,
thus perturbing the quality of the labor force (through equation 5) and the savings rate of the economy
(through equation 9). In addition, we consider the possibility that the HIV/AIDS prevalence rate
affects the aggregate level of productivity (see for instance Haccker, 2001). This may occur as labor
turnover and absenteeism increase and as firms divert resources to preventive activities. This can also
occur if economic efficiency decreases as resources are allocated to treat HIV/AIDS patients, or as
social capital is eroded. To formalize this idea we simply postulate that the realized level of
productivity is given by:
A: =At(l-diA,-d2cht), (10)
4 Notice that as long as c< IUlN = (I+ )'C+ N' iC -' fCiaN >o, so that a reduction in N, as a result of
HIV/AIDS, results in a welfare loss.
- 6-
where d1 and d2 are the parameters determining productivity losses, and cb is the share of HlV/AIDS
health related expenditures in GDP. In the presence of HIV/AIDS, A* replaces A in equations (1)
and (3) to (9). We notice that the implicit assumption behind this formulation is that HIV/A1DS does
not have permanent effects on total factor productivity. In other words, if the prevalence rate drops to
zero at time t, total factor productivity will return to its steady-state level (A*=A). This again is a
conservative assumption. (for a case where the impacts are permanent see MacFarlan and Sgherri,
2001).
Health expenditures are simply modeled by keeping track of an average expenditure per
patient (which depends on the level of GDP per capita) and a given level of access to health services.
We have:
Ch,t=( (Y, )sAH, )/ Y,X(1)
where X0 and XI are the parameters determining the average cost of treating a patient as a function of
GDP per capita (y), and X2 gives the share of the stock H of infected individuals who have access to
curative services.
Modeling the diffiusion of HIV/AIDS
As with any other infectious disease, the diffusion of HIV/AIDS depends on its reproduction
rate. This is the average number of people who are infected by a person carrying the virus during
his/her life span. The higher the reproduction rate, the faster the diffusion of the disease. Diseases
that have a reproduction rate lower than 1 gradually disappear. In the case of HIV/AIDS, the
reproduction rate is determined by the types of contagion mechanisms (e.g., sexual intercourse,
sharing of infected needles among drug users, mother to child, blood transfusion, or repeated use of
needles in hospitals and health centers), and three elements associated with each of them: the duration
of the period during which an individual can transmit the disease through the given mechanism; the
risk of transmission per contact; and the frequency and heterogeneity of contacts.
In this study we simulate the diffusion of the HIV epidemic trough two channels: sharing of
infected needles among IDUs and sexual intercourse. The former is based on Law (2001), while the
latter is based on the model AVERT developed in Rehle et al. (1998). AVERT was modified to
consider additional population groups (the original model has only two population groups) and to
introduce time (the original model is static)5.
Our model divides a country's population aged 15-49 into 5 groups: sex workers, IDUs
female, IDUs male, low-risk female, and low-risk male. In terms of infections related to needle
sharing, the model assumes that needles are shared at random (this assumption is introduced for
tractability). Hence, the probability that an uninfected IDU would become infected between time t-l
and t, is given by:
5 An alternative to the current model would have been a more complex agent-based model. Nonetheless, the
time needed for calibration, simulation, and analysis of agent-based models tends to increase considerably. The
current model is considered an adequate compromise. Moreover, AVERT, despite its simple formulation, has
shown to produce reasonable forecasts of infections averted as a result of interventions such as increasing
condom use or reducing the prevalence of STDs. For other non-agent models of the epidemic see Stover and
Way, (1995) and Stover (1997).
-7 -
Pr,, = I - Od Ldt (1-ridu )+ (1 d)]} ie ED V dE D, (12)
where D is the vector of IDUs population groups (in this case D has two elements: male and female),
*d iS the share of the population of group d in the total population of IDUs, P& is the H[V/A]DS
prevalence in the population group d, ridu is the probability of infection after an injection with an
infected needle, n is the total number of injections between time t- 1 and time t, u is the share of these
injections which use safe needles, and dU is a policy variable introduced to simulate reductions in
sharing practices. In equation (10) the expression between {} is the probability of remaining
uninfected after an injection with a shared needle. The probability that the needle will come from
group d is given by d Within this group, the probability that the needle is infected is given by 0,L the
HIV/AIDS prevalence rate in group d. Finally, if the needle is infected, the probability that the
individual will not acquire HIV is given by l-ridu . To obtain the joint probability of remaining
uninfected after n injections, taking into account that a share I-u-dU of these injections takes place
through shared needles, we simply raise the expression in brackets to the n(I -u-dU) power.
In the simulations, before considering infections due to sexual intercourse, the prevalence rate
for the IDUs population groups is updated on the basis of equation (10). We get:
ffdt = (H + Nd, (1-fd, ) Prd, )INd, (13)
where Hd, is the stock of HIV/A1DS infected individuals at time t in group d. At this stage deaths are
not yet taken into account.
In terms of infections sexually transmitted we follow the same mechanics than the model
AVERT. The probability that an HIV negative individual would become infected at time t is given
by:
Pr, =1-H{[0J{XEWs(I-r(- (f +dF)e)) + (I-j, iE J;je J ,;sE S, (14)
where J is the vector of population groups (5 in this case), j, is the HI V/AIDS prevalence observed in
group j at time t, S is a vector containing different STDs status (four cases are considered: non-STDs,
ulcerative STDs, non-ulcerative STDs, and both ulcerative and non-ulcerative), w, is the probability
of observing state s, r, is the probability of HD/AIDS transmission after intercourse in status s, f is
the probability of using condoms, e is the effectiveness of the condoms, mij is the average number of
sexual partners that individuals of group i have in group j, and n1j is the average number of
intercourses with each partner between time t and t+l. Similar to the case of equation (12), the
expression between {} gives the probability that an uninfected individual in group i would remain
uninfected after sexual intercourse with mij partners in group j. The product over the J groups gives
the joint probability of remaining uninfected after sexual intercourses with the partners in all groups.
Inside the brackets, the probability that the partner will be infected with HIV is approximated by the
prevalence rate ij. If the partner is infected, the probability of remaining uninfected after n,j
intercourses is given by the expression in parenthesis. The probability of infection is given by rs,
which depends on whether the partner suffers from an STD. As previously mentioned, the probability
of having different types of STDs is given by w,. Therefore, the sum gives the expected probability
-8 -
of remaining uninfected after one intercourse with a partner, taking into account the prevalence of
STDs.
Under these assumptions, the dynamics of the HIV/AIDS prevalence rate is given by:
A,+ = Hj, + NJ, (I - fl,, )Prj, - Aflj_-o N,f+, (15)
Calibrating models parameters
The model parameters can be grouped into three categories: economic parameters (those
influencing growth and the dynamics of labor markets); parameters determining the economic impact
of the epidemic for a given prevalence rate; and parameters affecting the diffusion of the epidemic
(see Appendix C for a summary of parameter values and a description of calibration methods). Key
economic parameters were estimated in order to replicate targets in terms of economic growth and
savings rates, on the basis of the World Bank Country Assistance Strategies for each of the countries
included in the analysis (World Bank 1997, 1999b, 1999c, 2000, 2001a, 2001b, 2001c, 2001d,
2002a). For the parameters determining the diffusion of the epidemic and potential economic impacts
(for instance the distribution of deaths among skilled and unskilled workers), given the lack of data,
we defined uniform probability distributions. The supports of these distributions were calibrated on
the basis of results from the literature, in particular the works of Haacker (2001), MacFarlan and
Sgherri (2001), Bonnel (2000), and Ainsworth and Over (1994), for economic impacts; Floyd and
Gilks (2001), United Nations (2001), and Cavallini et al. (2000), for estimates about health
expenditures; Jenkins et al. (2002) and Rehle et al. (1998), for sexual and drug use behavior; and
UNAIDS and Jenkins and Robalino (2002), for initial prevalence rates.
2. Simulating the costs of inaction
Given high levels of uncertainty in terms of model parameters traditional simulation methods,
where outcome variables are analyzed for a handful of scenarios, are not appropriate. Here we adopt
an exploratory modeling approach (see Bankes, 1995). Hence, for each country, we sample a large
number of points in the parameters space from the uniform probability distributions described in the
previous section (our priors). At each of these points we compute the value of five variables of
interest: the present value of total GDP and its average growth rate during the period 2000-2025; the
size of the population in year 2025; and the HIV/AIDS prevalence rate and HIV/AlDS related health
expenditures in year 2015.
As an illustration, Figure 1 graphs losses in the present value of total GDP and the HIV/AIDS
prevalence rate for 100 sampled points in the case of Tunisia. We observe that there is a wide range
of plausible futures (see Appendix D for a sample of diffusion profiles). The HIV/AIDS in year 2015
could fluctuate anywhere between 0.18% and 15%, while losses in the present value of GDP for the
period 2000-2025 could be equivalent to 2% to 300% of today's GDP. Clearly, not all the futures
have the same likelihood. For instance, in 35% of the cases explored the HIV/AIDS prevalence rate
in year 2015 would be below 2%. That being said, only in 16% of the cases the HIV/AIDS
prevalence would be below 1%. In fifty percent of the cases, the prevalence rate would be above
3%.
-9-
Figure 1: Tunisia: plausible futures for the H1IV/AIDS prevalence rate and losses In GDP
(2000-2015)
2%IX0%
1J150%
MO1~% - 11
8 50.0%
0.0% ~ 00% I 2L9
0.0% 50# 10.0% 150% 20.0%
HIV/AIDS prevalence rate In year 2015
The same type of analysis was conducted for the 9 countries included in the study. For each
country we summarize the distribution of the output variables of interest by reporting the mean,
standard deviation, and the minimrum and maximurn values (see Table 1). Graphics of the full
distributions are provided in Appendix E. All distributions deviate from the normal distribution, and
therefore one needs to be careful when interpreting the means.
- 10-
Table 1: Descriptive statistics for Selected Output Varables (Status-quo)
Average GDP Population Health
pvGDPI2000-20251 loss (% growth rate (2000- change In 2025 HIV prevalence expenditures 2015
Country Statistic today's GDP) 2025) (%/ ) 2015 (*/*) (% GDP)
Algeria Mean 36.2% -0.32% -3.5% 3.90% 1.3%
std 36.7/o 0.37/o 2.8% 3.5% 1.2%
min- 4.5% -2.39% - 13.7P/ 0.4% 0.1%
max 222.2% -0.03% -0.6% 17.1% 5.8%
Djibouti mean 227.1% -1.64% -22.2% 18.3% 6.4%
std 121.5% 1.24% 10.6% 12.4% 4.4%
min 85.2% -7.19% -53.6% 2.5% 0.9%/0
max 698.2% -0.36% -8.2% 55.3% 19.9%
Egypt mean 44.3% -0.33% -3.2% 3.8% 1.3%
std 47.6% 0.40% 2.6% 3.3% 1.1%
min 4.4% -2.61% - 12.7/o 0.4% 0.1%
max 285.4% -0.03% -0.5% 16.1% 5.5%
Iran mean 33.6% -0.33% -3.3% 3.7% 1.3%
std 36.0% 0.41% 2.6% 3.3% 1.1%
min 3.3% -2.67/o -12.8% 0.4% 0.1%
imax 215.8% -0.03% -0.5% 15.9% 5.4%
Jordan mean 27.90% -0.270/o -2.6% 3.2% 1.1%
std 31.0% 0.34%0 _ 2.1% 2.8% 1.00h/
nmin 23% -2.180% -10.5% 0.3% 0.1%
max 187.8% -0.02% -0.3% 14.00/o 4.8%
Lebaon mean 26.3%. -0.36% -3.8% 4.1% 1.3%
std 26.90/o 0.43% 3.0% 3.6% 1.2%
min 3.1% -2.77/o -14.8% 0.4% 0.1%
max 161.8% -0.03% -0.6%0 . 17.5% 5.7%
Morocco mean 33.2% -0.33% -3.3% 3.7/o 1.3%
std 35.3% 0.400/o 2.6% 3.3% 1.1%
miin 3.5% -2.55% -13.1% 0.4% 0.1%
- max 211.5% -0.03% -0.5% 16.0% 5.4%
Tunisia mean 45.5% -0.34% -3.5% 3.8% 1.2%
std 47.70/ 0.41% 2.8% 3.3% 1.1%
min 5.2% -2.62% -13.P/o 0.4% 0.1%
max 286.1% -0.03% -0.5% 16.4% 5.3%
Yemen mean 3 1.4% -0.27/o -2.5% 3.3% 1.2%
std 34.8% 0.33% 2.0% 2.9% 1.1%
min 2.6% -2.170/o -9.8% 0.3% 0.1%
nmax 211.7% -0.02% -0.3% 14.1% 5.2%
The level of uncertainty surrounding the dynamics of HIV/AlDS is considerable. Given that
all countries start from similarly low baselines, the range of variation of the prevalence in year 2015
is similar (between 0.3% and 17% excluding Djibouti). Across countries, losses in the present value
of GDP per capita could range between close to 2.3% of today's GDP to over 1000/0 (in Djibouti
maximum impacts could be equivalent to 7 times current GDP). Average real GDP growth rates for
the period could be reduced by 0.02% to 2.7% per year. The size of the population in year 2025
could be reduced by 0.3% to 15%, and HIV/AIDS related expenditures could increase by 0.1% to 6%
of GDP in year 2015.
On average, impacts are likely to be considerable. Excluding Djibouti, the average
HIV/AIDS prevalence rate in year 2015 across scenarios approximates 4%. The GDP growth rate for
the period 2000-2025 could be reduced, on average, by 0.27% per year in Yemen and Jordan, to
0.36% in Lebanon, and over 1.6% in Djibouti. Lower economic growth would result in output losses
for the period 2002-2025 equivalent to 31.4% of today's GDP in Yemen, 36% in Algeria, 44.3% in
Egypt, and 227% in Djibouti. In year 2015, HIV/AIDS related health expenditures could average
1.2% of GDP. By year 2025, the labor force could have been reduced by 2.5% in Yemen to 4% in
Algeria and 22% in Djibouti. Thus, regardless of the distribution of factors that affect the
vulnerability of different countries (differences in unemployment rates, the share of labor in total
inputs, the growth rate of labor productivity), HIV/AIDS poses a considerable treat.
The HIV/AIDS epidemic presents a typical problem of decision making under conditions of
deep uncertainty. Prevalence rates could remain at low levels, but there is also a risk that the
epidemic reaches alarming levels and causes not only economic damage, but also grieve as
individuals in their most productive ages die. The next section discusses the type of policy
interventions that could be implemented to insure societies against these risks.
3. Social gains from preventive interventions
It has been extensively discussed in the policy arena that governments have a key role in
developing and financing the implementation of policies to confront HIV/AIDS. Indeed, individuals
alone could not devise appropriate mechanisms to contain the epidemic. First, individuals do not take
into account the social costs of the risks they take, or the social benefits of the preventive measure
they adopt. In an unregulated market we would observe an excess of risky behavior and too little
prevention from a social point of view. A second reason is given by information problems.
Individuals may not have enough information about the risks of HWV and may lack knowledge about
preventive behaviors. Finally, cultural/religious values may constraint individuals' actions in ways
that render them, and society, more vulnerable to HIV/AIDS. The role of Governments in providing
information and subsidizing interventions to reduce risky behaviors is therefore critical.
Governments can only intervene, however, if there are cost-effective interventions at their
disposable. Fortunately, international experience has demonstrated that this is the case with
HIV/AIDS. Recent studies show that interventions which focus on reducing risks (through
information and preventive behaviors and services) in those population groups most likely to contract
and spread HIV can be highly cost-effective (see Kahn, 1996). Interventions such as reproductive
health and HIV/AIDS education in schools, targeted STD treatment for highly vulnerable groups, and
harm reduction for IDUs have also proved to be cost-effective (see Jenkins, 2002). In general early
interventions bring higher benefits and lower costs.
The question of interest for policymakers in MENA countries, where prevalence rates remain
low and future dynamics are highly uncertain, is whether there are still interventions that governnent
-12-
could/should implement. We focus on two classical interventions: condoms distribution and
expanding access to safe needles for IDUs. As previously discussed, heterosexual transmission and
transmission through the sharing of infected needles among IDUs are the two major mechanisms that
could sustain the development of the epidemic.
To implement these policies we affect the parameters dU (equation 12) and dF (equation 14),
which are respectively the reduction in the probability of sharing a needle and the increase in the
probability of using a condom. The total costs of these interventions is given by:
T
cos t(dF, dU, s) = E p[sex(t).dF.p, + drugs(t).dU.p],' (16)
I=s
where s is the time when the policy is in effect, sex(.) is a function giving the total number of sexual
contacts, drug(.) is a function given the total number of needles consumed (both defined on the basis
of equations 12 and 14), and Pc and pn are respectively the average costs of distributing a condom and
a needle.
We simulate two policies that are summarized in Table 2. In both cases, dU is set equal to
0.20 (that is the probability of sharing a needle is reduced by 20 percentage points) and dF is set equal
to 0.30 (the probability of using a cotndom increases by 30 percentage points). The first case sets s=1
(i.e., the policy -is implemented immediately), while the second case sets s=5 (the policy is
implemented after 5 years). Since we prefer to underestimate benefits and overestimate costs, we
work with high-end estimates from the literature. Thus, we assume that the average cost of
distributing a condom is equal to USD 0.5 while the average cost of distributing a needle is equal to
USD 1.5 (see Table 3).
Table 2: Two interventions: expanding condom use and access to safe needles for IDUs
Policy A Policy B
Increase in condom use (dF) 30% 30%
Reduction in needle sharing (dU) 200/o 20%
Year when the policy is implemented (s) 1 5
[Average cost condom in USD (Pc) 0.5 0.5
[Average cost needle in USD (Pn) 1.5 1.5
Table 3: Unit costs for a needle distribution intervention
Input Cost Unit
Cost needle and syringe 0.1 per unit
Rent drop-in center 165 month
lectricity drop-in center 35 month
Supervisor drop-in center 3,000 year
Cost distributor 2 day
We assume that all costs are directly subtracted from GDP6. For each sampled point in the
parameter space we then re-compute the same five output variables than in the non-intervention case.
6 At the macro level this is not necessarily the case, since the production and distribution of condoms and
needles can also contribute to GDP. A more realistic approach, but less conservative, would have been to look
at the distortionary effects of reallocating resources to the production and/or importation and distribution of
needles and condoms.
- 13 -
In Table 4 we summarize the new set of descriptive statistics when the policy is implemented
today, as well as changes in the means with respect to the non-intervention cases and the standard
error of the estimate of this change (in italics). We measure the efficiency of the policy intervention
by its impact on total GDP. We observe that, averaging across scenarios, GDP losses during the
period 2000-2025 are significantly reduced, by an equivalent of 15.5% of today's GDP in the case of
Jordan, to 27% in the case of Egypt, and 71% in the case of Djibouti. This is after taking into account
the costs of distributing condoms and needles. Lower losses result from a significant reduction in the
average H1V/AIDS prevalence rate in year 2015 (minus 2.8 percentage points). We observe that the
minimum and maximum loss (that is the extremes of the support of the distribution) are reduced as
well. The latter in particular is reduced by an equivalent of 100% of today's GDP in the case of
Yemen, to 200% in the case of Egypt. This is a clear illustration of how to mitigate the risks
associated with HIV/AIDS. In other words, even if the likelihood of each scenario remains de same,
the loss in each case would be considerably reduced. Implementing the policy can then be considered
as a form of insurance against the risks of the epidemic.
The policy intervention also produces statistically significant reductions in population losses
and health expenditures. In most countries the total population in year 2025 could be 2.4% higher
than in the case of the status-quo. In Djibouti, the total population would be, on average, 9% higher.
Health expenditures related to the treatment of HIV/AIDS, on the other hand, would be reduced by
close to one percentage point of GDP.
A clear message from these results is that, in the face of an uncertain future regarding the
dynamics of the HIV/AIDS epidemic and its economic impacts, countries in the MIENA region will
be better-off if interventions such as the ones simulated here are adopted.
- 14-
Table 4: Descriptive statistics for Selected Output Variables (Policy A)
HIV Health Loss
pvGDPI2000-20251 Average GDP Population prevalence expenditures GDP
loss (% today's growth rate change In year 2015 2015 (% Loss growth
ountry Statistic GDP) (2000-2025) 2025 (%) (°/O) GDP) pvGDP rate Pop HIV Health
Ageria mean 15.8% -0.1% -1.2% 1.2% 0.4% - 0.20! 2.3/ -2.8! -0.99/
std. dev. 12.70% 0.1% 1.0% 1.2% 0.4% 0.104 0.00 0.0"A 0.0"A 0.0
min 2.9% -0.6% -5.9% 0.0% 0.0°/° -1.6°/ 1.8"! 7.80/ -0.44! -0.1"!
max 70.5% 0.0% -0.1% 6.9% 2.3% -151.6°/ 0.0"! 0.5"! -10.1"! -3.4/
Djibouti mean 155.8% -0.8% -13.5% 8.2% 2.90%o 0.8"! 8.7"! -10.1"! -3.5"!
std. dev. 69.5% 0.6% 5.8% 6.6% 2.3% 1.60 0.0S 0.0S 0.0S 0.O"
min 76.7/o 4.0% -33.7% 0.8% 0.3% -8.5"! 3.2"! 19.9"! -1.7"! -0.6"!
max 485.1% -0.3% -6.3% 3 1.9% 11.3% -213.0! 0.1 " 1.9"! -23.4"! -8.6!
Egypt mean 16.8% -0.1% -0.9% 1.0% 0.3% ANN 0.2"! 2.4"! -2.8"! -0.9"!
std. dev. 14.9% 0.1% 0.9% 1.1% 0.4% 0.2S 0.0"S 0.0" 0.0" 0.0"
min 2.4% -0.6% -5.5% 0.0% 0.0% - 1. 9! 2.0"! 7.3"_ -0.4" -0.1"
max 82.00 o 0.0% 0.0%!0 6.6% 2.2% -203.4"° OO." 0.5Y -9.5"! -3.2"
Iran mean 13.0% -0.1% -0.9%h 0.9% 0.3% _ 0.2"! 2.4" -2.8" 0.9"
std. dev. 11.3% 0.1% 0.9% 1.1% 0.4% O.I" 0.0" 0.0" 0.0" 0.
min 2.0% -0.6% -5.5% 0.0% 0.0% -1.30Y 2.1°! 7.3! -.4"! -0.1"
max 62.1% 0.0% 0.0% 6.5% 2.2% -153.7"! 0.0 " O 0.5" -9.4Y -3.2
Jordan mean 12.4% -0.1% -0.7% 0.9% 0.3% 0.0. 2"! 1.9"! -2.4! -0.8"!
std. dev. 10.1% 0.1% 0.7% 0.9%/0 0.3% 0.1" 0.0" 0.09 0.0a 0.0
min 2.3% -0.5% -4.5% 0.0% 0.0% 0.0"! 1.7"! 6.00! -0.3"!4 -0.!4
max 55.4% 0.0% 0.0% 5.8% 2.0% -132.3"! 0.0" 0. 3"! -8.2"! -2.8"!
Lebanon mean 9.5% -0.1% -1.1% 1.1% 0.4% .; 0.3"! 2.70! -3.0"! - 1.0"
std. dev. 8.4% 0.1% 1.0% 1.2% 0.4% 0.1" 0.0s 0.0"o 0.0 0.0a
min 1.5% -0.6% -6.3% 0.0% 0.0% -1.6"! 2.1"! 8.4"! -0.4"! -0. I
max 45.6% 0.0% -0.1% 7.1% 2.3% -116.3"! 0.0"! 0.5" -10.4"! -3.4"!
Morocco mean 13.1% -0.1% -0.9%/ 0.9% 0.3% 0.2" 2.4"! -2.8Y -0.9"!
std. dev. 11.2% 0.1% 0.9% 1.1% 0.4% 0.1" 0.0" 0.0" 0.0o .0o,
min 2.2% -0.6% -5.6% 0.0% 0.0% -1.40" 2.0"! 7.59 -0.44! -0. 1"
max 62.6% 0.0%/0 0.0% 6.6% 2.2% -148.9"! 0.0"! 0.4"! -9.4"! -3.2"!
Tunisia mean 15.1% -0.1% -1.0%/ 1.0%!o 0.3% W 0.3%! 2.5"! -2.8"! -0.9"!
std. dev. 14.4% 0.1% 1.0% 1.1% 0.4% 0.2" 0.a0" 0.s .o a0
nmin 2.0% -0.6% -5.9% 0.0% 0.0% -3.2"! 2.0"! 7.89! -0.4"! -0.1"!
max 80.8% . 0.0%!o -0.1% 6.7% 2.2% -205.3Y 0.0"! 0. 5" -9.6"! -3.1"!
Yemen mean 29.2% -0.1% -0.8% 1.0% 0.4%0/ 0.1"! 1.7"! -2.3" -0.8"
std. dev. 16.7% 0.1% 0.7% 1.0% 0.4% 0.1" 0." 0. 0.0"o 0.0
_ _ min 8.7%/ -0.6%/ -4.2% 0.0%/ 0.0%/ 6.0"!/ ISV 5.6Y~ -0.3"!/ -0.1"!X
max 94.8% 0.0% 0. Om" 5.7% 2.1% -116.9"!j 0. 3° 0.3 -8.4"! -3.1"!
What are the social costs of delaying action? This a second question that policymakers would
like to have answered. Here- we have simulated the impact of a relatively small delay in the policy
intervention: 5 years. The new descriptive statistics are summarized in Table 5. The results suggest
that delaying the intervention for 5 years could cost an equivalent of 4% of today's GDP in the case
of Yemen and Jordan, to 8% in the case of Egypt and Tunisia, and 20% in the case of Djibouti. Five
years of inaction would increase the average HP//AIDS prevalence rate in year 2015 by 0.4 to 0.6
percentage points. This represents more than 60,000 additional infections in the case of Algeria,
- 15 -
more than 100,000 in Egypt, and over 400,000 adding across the 9 countries included in this analysis.
Thus, the costs of delaying action are considerable. Governments ought to intervene today when
HIV/AIDS prevalence rates are still low.
Table 5: Descriptive statistics for Selected Output Variables (Policy B)
Average HIV Health
pvGDP[2000- GDP opulation prevalence expenditures Loss GDP
20251 loss (% growth rate change in year 2015 2015 (% Loss growth
Countr Statistic todan's GDP) (2000-2025) 2025 (%) (%) GDP) vGDP rate Pop HIV Health
Algeria mean 21.5% -0.1% -1.8% 1.7% 0.6% .6__ t -0.0350! 0.5960/ 0.5010/ 0.1 690!
I____std. dev. 17.5% 0.1% 1.4% 1.6% 0.5% 0.03590 0.000"4 0.000" 0.0000 0.000"
min 4.0% -1.0% -7.2% 0.1% 0.0% -2.401°/! -0.3320! 1.2840! 0.074°! 0.0250/
imax 115.3% 0.0% -0.2% 8.1% 2.7% -0.013°/c -0.0050/ 0.123Y/ 1.2000/ 0.405°/
Djibouti mean 176.0% -1.0% -15.5% 9.4% 3.3% C 9m -0.1 150"! 2.053"! 1. 1 730/ 0.412°/
std. dev. 81.3% 0.7% 6.8% 7.4% 2.6% 0.827A 0.000R 0.006O 0.007" 0.001A
rnmi 81.3% -4.4% -38.5% 0.9% 0.3% -7.907°/c -0.4220/ 4.722"! 0.1090/ 0.038"/
max 544.4% -0.3% -6.9% 35.4% 12.5% -0.3420/ -0.0230! -0.5620/ 3.4430/ 1.247°!
Eyt mean 25.1% -0.1%, -1. 6%/ 1.6% 0.5% 1 -1 N I -0.045"!4 0.698Y/ 0.6410"! 0.217"
std. dev. 21.8% 0.1% 1.3% 1.5% 0.5% 0.053" 0.000" 0.000" 0.000" 0.000O
nmin 4.1% -1.0%!o -6.7% 0.1% 0.0% -2.594"/c -0.416"! 1-.264! 0.0970! 0.0330/c
max 143.7% 0.0%!o -0.2% 7.8% 2.6% -0.011°/ -0.0070! 0.1490! 1.2020! 0.4070/
Iran mean 19.2% -0.1% -1.6% 1.6% 0.5% MVA -0.0440! 0.692° 0.6310! 0.2130/
std. dev. 16.6% 0.1% 1.3% 1.5% 0.5% 0.031% 0.00OA 0.000" 0.000" 0.000"
mnin 3.3% -1.0% -6.8% 0.1% 0.0% -2.657"! -0.427"! 1.245! 0.095"! 0.032"!
max 109.5% 0.0% -0.2% 7.7% 2.6% -0.011!/ -0.0060! 0.146° 1.171"! 0.397!/
Jordan mean 16.7% -0.1% -1.2% 1.3% 0.4% _fi5c -0.029°! 0.454! 0.457"! 0.157"!
std. dev. 14.3% 0.1% 1.0% 1.3% 0.4% 0.023" 0.000" 0.000" O.0000 0.R000
m______ win 2.5% -0.8% -5.5% 0.0% 0.0% -2.182%! -0.328"! 1.005"! 0.021"! 0.007"!
max 93.9% 0.0% 0.0% 6.8% 2.3% -0.007"/! -0.001 4.024" 1.003"! 0.344"!
Lebanon mean 14.5% -0.1% -1.9% 1.8% 0.6% i c -0.048"! 0.792"! 0.684"! 0.223"!
_ std. dev. 12.5% 0.2% 1.5% 1.6% 0.5% 0.0017°A 0.000A 0.000 0.000 0.000"
min 3.0% -1.1% -7.7% 0.1% 0.0% -2.7660/c -0.4560! 1.400° 0.122"! 0.0400!
max 82.90% 0.0% -0.3% 8.4% 2.7% -0.015/! -0.010"! -0.226! 12.2700! 0.415"!
Morocco mean 19.2% -0.1% -1.6% 1.5% 0.5% -0.044°! 0.666" 0.609"! 0.206"!
std. dev. 16.40% 0.1% 1.3% 1.5% 0.5% 0a030 0.000 0.000" 0.0 0-0.000"
min 2.90/o - 1.0%W -6.90/o 0.1% 0.0% -2.554°/c -0.397° 1.300"! 0.057° 0.019"!
max 107.3% 0.0% -0.1% 7.8% 2.6% -0.010/c -0.004"! 0.086 1.225"! 0.416"!
Tunisia mean 23.2% -0.1% - 1.7/o 1.6% 0.5% ';. -0.046"! 0.690"! 0.618"! 0.201"!
std. dev. 21.3% 0.1% 1.3% 1.5% 0.5% 0.050"4 0.000" 0.00 0.000"4 0.0005"
nmin 3.0% -1.0% -7.3% 0.1% 0.0% -2.617/! -0.408Y 1.357"! 0.059"! 0.019"!
max 139.8% 0.0% -0.2% 8.0% 2.6% -0.010! -0.004"! 0.090" 1.255"! 0.409"!
Yemen mean 33.2% -0.1% -1.2% 1.4% 0.5% -0.022" .389"! 0.373! 0.O137"!
____std. dev. 20.5%- 0.1% 1.0%/ 1.3% 0.5% 0.051 0.0 0.00 o0" 000" .000
min 9.2% -0.90/ -5.1% 0.1% 0.0% -2.249"! .0241° 0.893"! 0.048"! 0.017"!
_______ max 132.1% 0.0% -0.1% 6.6% 2.4% -0.020! -0.003" 0.066! 0.9 19"! 0.338°!
-16-
4. Conclusions and discussion
This paper develops a model of optimal growth coupled to a diffusion model of the
HIV/AIDS epidemic based on two transmission factors: sexual intercourse and exchange of infected
needles among IDUs. The model is used to assess the risks of a major HIVtAIDS epidemic and
expected economic impacts. The paper argues that the necessary conditions to support the diffusion
of HIV/AIDS are present in MENA countries. Clearly, high levels of uncertainty pervade any
projection of prevalence rates. Thus, in the simulations we explore large regions of the parameter
space and derive distributions for the dynamics of 5 variables of interest: the HIV/AIDS prevalence
rate, total GDP, the growth rate of GDP, total labor force, and HIV/AIDS related health expenditures.
The results show that in only 16% of the cases analyzed would prevalence rates in year 2015 be
below 1%. On average, GDP losses across countries for the period 2000-2025 could approximate
35% of today's GDP. However, in all countries it is possible to observe scenarios where losses
surpass today's GDP. So while HIV/AIDS prevalence rates could remain low, there are also risks
that they continue to increase and in this case economic costs can be considerable.
The analysis shows, however, that there are policies that governments could put in place to
insure against these risks. The two policies considered in this study (expanding condom use by 30%
and expanding access to clean needles for IDUs by 20%), could reduce GDP losses across the 9
countries by an average of 19% of today's GDP. The analysis also shows that delaying action can be
considerably costly. For instance, waiting for 5 years before intervening could cost an equivalent of
6% of today's GDP.
It is important to note that in this paper we have only looked at two types of interventions that
are not tailored to specific countries. Clearly, the total amount of resources that societies ought to
invest to fight HIV/AIDS and the allocation across alternative interventions depend on countries'
characteristics. Indeed, the costs and effectiveness of the different interventions are given by factors
such as the level of development of the epidemic, social and economic constraints on safe behavior,
underlying pattems of sexual and drug-injecting behavior, local costs, and implementation capacity.
Several methodologies are available to guide these allocations (see Kaplan and Pollack, 2000). These
methodologies, however, require baseline data. Therefore, one of the priorities among MENA
countries is to strengthen surveillance and information systems. Strategies would then need to be
designed with strong involvement from civil society. Programs that are implemented should be
carefully monitored to evaluate costs and impacts, thus allowing adjustments/corrections when
necessary.
Another limitation of our analysis is that it doesn't shade light on the question of how the
epidemic affects different population groups, in particular the poor. This is important when designing
policy interventions. There is some evidence that the economic impact of the epidemic is likely to be
higher among the poor, because their main or only source of revenue is their labor force (the non-poor
can hedge with other assets losses in wage-income related to AIDS). Furthermore, coping
mechanisms for the poor are more limited and usually involve changes in consumption patterns (e.g.,
reducing education, food, and health expenditures) or sending children to work. These mechanisms
produce human capital losses as a result, among others, of high child malnutrition or lower school
enrollment rates. While in MENA countries informal coping mechanisms to manage risks are diverse
- ranging from family support and kinship ties to religious charitable organizations - research has
shown that they are usually insufficient to hedge against adverse shocks (see World Bank, 2002b).
Studies show that reductions in consumption in low-income households following the death of an
adult household member would reduce food expenditures by 32% and food consumption by 15% (see
Over et al., 2001). This occurs not only as household income is lost and funeral expenditures need to
-17-
be financed (on average households spend 50%/o more, $800-$900, on funerals than they do for
medical care), but also because households that experience a death cut back on the number of hours
they work for wages (Beegle, 1996). In most MENA countries, the poor already face problems of
access to health services As health systems become financially constrained, these problems can be
exacerbated. At the same time, the poor are more exposed to infectious diseases and complicated
with malnutrition, and thus are more vulnerable to the deterioration of their immune system.
With all its limitations, four important conclusions can be derived from the analysis
developed in this paper: i) the risk of an increase in the HIV/AIDS prevalence rate in MENA
countries is real; ii) expected costs over the next 25 years could be considerable; iii) there are actions
that can be implemented to prevent the spread of the epidemic and the costs of these actions would be
more than compensated by the savings they generate; and iv) the time to act is today when prevalence
rates are still low.
- 18-
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- 21 -
Appendix A: A caveat about savings and current international evidence
In the current version of the model, HIV/AIDS does not have any impact on the parameters of
the utility function; the coefficient of risk aversion and the discount rate are fixed. Nonetheless, from
a micro-economic perspective there are reasons to believe that this is not necessarily true, and that in
fact the epidemic could increase aggregate savings. To see this consider the following problem faced
by a representative consumer trying to allocate consumption over two periods.
Maxco U(co)+ pU(c1)
sit. , (Al)
c (co)= w, -h+(wo -co)(I+r)
where U(.) is a utility function with decreasing marginal returns to consumption, c is consumption, p
is the discount factor, w stands for wage related income, h are health expenditures during the second
period (the assumption is that the individual is healthy during the first period), r is the interest rate
received on savings, and the indexes identify the period.
The first order condition of the maximization problem is given by:
au= P pau ac, (A2)
acO act aco
which states that optimality implies equating the discounted marginal utility of consumption between
the two periods.
From the assumption of decreasing marginal returns we get the following results:
D uac, A auaC, auac,A
a- a _ a _
alu < clc o ac'aco , O;_ aclaO ,o
-j2U < C1 >0) > 0; > 0
acoa aco ash ap
The graphical representations of these partial derivatives is given in Figure Al. The
implication for our study is that HIV infected individuals expecting higher health expenditures at time
2 (as a result of HIV/AIDS), would experience an increase in the expected marginal utility of
consumption (the right hand side of equation A2), and therefore reduce consumption today.
Intuitively, these individuals will save for bad times. Similarly, an increase in the discount rate as a
result of shorter life expectancy (i.e., a reduction in the right hand side of equation A2) would
increase consumption today and therefore reduce savings. These ideas are summarized in the Figure
Al. Given uncertainty about the effect that HIV/AIDS has on the parameters of the utility function,
we have opted for keeping them constant.
- 22 -
Figure Al: Changes in savings rates resulting from the HIV/AIDS epidemic
Marginal utility An increase in h
reduces CO Marginal utility of
consumption at time I
A decrease in p (i.e., a
higher discount rate)
increases consumption
Marginal utility of
consumption at time 0
Optimal consumption Consumption at time 0
- 23 -
Appendix B: Solving the optimization problem
To solve the problem we re-write equation (1) as:
Y, = (TN)'-e Kt" (B1)
We recognize that the growth rate of T and N decreases over time. We approximate the
dynamics of these growth rates by:
T' = Yr exp(-6T,t) (B2)
N= YN exp(- Nt), (B)
where the parameters Yr, YN., °r, and ON, are estimated by OLS for each scenario regarding the
diffusion of the HIV/AIDS epidemic, which affects the composition and size of the labor force and
therefore the dynamics of q, A, T, and N. There are no scenarios where the growth rate of N is
negative (this is consistent with international evidence, see World Bank, 1999).
The optimization problem to be solved then has t the same formulation than Pfizer (2000) and
Robalino et al. (2002). At the optimum the parameters al and a2 in equation (9) are given by:
a, = YT a a2 Ink
at2 = (fl - f2)/ f3-1
where:
f, = exp(yr + yn ) + X + Ict (ic/T)
f2 = VeXp(YT + Y, )+ CKk + IC (KCk / r)2 -4 exp(yr + Yn )ckk
f3 =2exp(yT + y.)
Kkc =-c / k
k= (1 + p) (6-1)6k' exp(-y T)
iAc =(I+ p)exp(rrr)
c= k +(I-6 -exp(rT + yn,))i
-= [((I + p)exp(yTTr)-(1- 6k))1 ]o-I
- 24-
Appendix C: Calibrating model parameters
The different model parameters are summarized in Tables Cl to C3. Table Cl is divided in
three sections. The first section defines economic parameters (those determining growth and the
dynamics of labor markets). The second section defines parameters determining the economic impact
of the epidemic for a given prevalence rate. Finally, the third section concerns the parameters
affecting the diffusion of the epidemic. Tables C2 and C3 present additional parameters affecting this
diffusion. We discuss each of these in turn.
Economic parameters
Economic parameters are grouped in two categories: growth parameters and labor market
parameters. Among the 10 growth parameters three were defined exogenously and are fixed across
countries: the depreciation rate of capital (8k), the discount rate (p), and the coefficient of risk
aversion in the utility function (T). The other parameters (in bold and italics) along with one of the
labor market parameters (the share of unemployed workers who find a job) were estimated in order to
achieve targets in terms of medium and long-term economic growth, investment targets, and
demographic projections. We proceed as follows. The growth rate of the labor force (YN) and the
change in the growth rate of the labor force (8N) were estimated basis of the World Bank official
country projections (World Bank, 2002). The remaining parameters, the labor productivity growth
(XA), the change in the growth rate of labor productivity growth (8A), the coefficient of the labor
factor in the production function (0), and the share of unemployed workers who find a job (nl3), were
estimated by solving the following optimization problem:
Minr a , :[;(1-5)-(Y,/Y) +[(5-15)-(Y,,Y5" f+[(15-25)-(Y2,l5)I(I-25)-l
where g(t-z) gives targets for the average growth rate during year t and year z, and I(t-z) gives targets
for the average saving rate during the period t-z. These targets were set for each country of the basis
of the Country Assistance Strategies (World Bank 1997, and World Bank 1999b to World Bank 2002
a). Medium termn growth targets are based on the Country Assistance Strategies. Long-term growth
rates increase the medium term targets by 1%. Investment rates are based on the last 10 years
average. When this average is below 20%, 20% is used instead.
In terms of labor market parameters, initial unemployment rates come from World Bank
Country Assistance Strategies (World Bank 1997-2002). For simplicity, the initial share of skilled
workers is arbitrarily set equal to 500/% and kept fixed across countries. The share of unskilled labors
is then computed by difference. We take an optimistic stance assuming that new entrants in the labor
force are not unemployed and that a majority, 70%/, are skilled. We do not allow mobility across
labor types (except from the unemployed group to the skilled).
The initial conditions for the output variables are set on the basis of the World Bank SIMA
(World Bank, 2002) and in the case of the capital output ratio (COR), using the World Bank TFP tool
kit (see Mahajan, 2001).
Parameters affecting the impact of the epidemic
There are three sets of parameters determining the economic impact of the epidemic. First
the parameters affecting the distribution of HIV/AIDS related deaths among labor types. Second, the
- 25 -
parameters determining HIV/AIDS related health expenditures and the impact on productivity, finally
the parameter determining the direct impact of the HIV/AIDS prevalence on labor productivity.
In terms of the distribution of AIDS related deaths, given little or no data in that respect, we
allow the share of skilled workers to vary between 20% and 50%. The higher this share, the larger
the economic impact. By constraining the upward bound to 50%/0 we are taking a conservative stance.
The remaining deaths are equally distributed among low-skilled and unemployed workers.
The calculation of potential changes in health expenditures is based on estimates from the
literature. In terms of the average cost of treatment, estimates from cross-country studies suggest a
range of 2-3 times GDP per capita (see Floyd and Gilks, 2001; United Nations 2001; Cavallini et al,
2000). In this analysis we assume that the average yearly cost of treating an HIV/AIDS patient is
equal to 1,400 USD in a country with a GDP per capita of $1,000 and that it increases by 0.95% for
each 1% increase in GDP per capita. Access to treatment, on the other hand, varies widely across
countries. For our calculations we assume that only a modest 300/o of those affected by AIDS would
obtain treatment. For simplicity we neglect the costs that health has on productivity.
Finally, the parameter defining the direct impact of H1V/AIDS on labor productivity is
allowed to vary between 0 and 0.5. This implies that a 1% prevalence rate can reduce the growth rate
of labor productivity by up to 0.5 percentage points. This is the low range from estimates in the
literature (see Haccker, 2001; and MacFarlan and Sgherri, 2001).
- 26 -
Table Cl: Model parameters
A!geri. D0ho. d EnD Iran Jordan Lebanon Morocco Trnisbi Yemen
ECONOMIC PARAMETERS
Growth paramters
deltaK 0.04000 0.04000 0.04000 0.04000 0.04000 0.04000 0.04000 0.04000 0.04000
deAIaA 0.00500 0.00500 0.00500 0.00500 0.00500 0.00500 0.00500 0.00500 0.01000
dehaN 0.05200 0.01416 0.03627 0.07246 0.04181 0.11327 0.05036 0.08827 0.01323
1mbdo. 0.03690 0.00386 0.04017 0.01937 0.01360 0.02734 0.02617 0.05357 0.00100
ambdaoN 0.03162 0.02396 0.02350 0.03538 0.03951 0.03575 0.02220 0.02705 0.03868
rho 0.06000 0.06000 0.06000 0.06000 0.06000 0.06000 0.06000 0.06000 0.06000
thau 0.90000 0.90000 0.90000 0.90000 0.90000 0.90000 0.90000 0.90000 0.90000
theta 0.48337 0.24010 0.33260 0.27609 0.32662 0.34267 0.36577 0.42584 0.20954
a1f1 0.31056 0.23486 0.18673 0.15346 0.16536 0.26148 0.23544 0.47009 0.43993
fa&2 -0.14861 -0.11626 -0.07217 -0.08636 -0.06166 -0.16948 40.08978 40.28734 -0.40277
Labor mlrkets
Share unemployed 21% 40%/ 11% 15% 10%/ 15% 20/o 15% 30Y0
Share skilled labor 50%1 50%1. 50%. 50°/. 50%0 50°/O 50°/O 50%l, 50°1%
Share unemnployed in new labor (0/% 0%/0 0%/0 0°/ 00/% 0% 0%/ 0% 0%/.
Shaue skilled in new labor 700/o 70%/o 700/o 70%/o 700/o 70°/ 70%/. 70% 70%/.
Shaeunenhployed whofld ailob 1.27% 0.13% 1.46% 0.38% 0.40%/a 0.53% 0.58%/ 0.99% 0.01%
Consrants
s(2001-2010) 0.27827 0.20000 0.24458 0.20000 0.25000 0.20500 0.25000 0.27849 0.20000
g(2000-2005) 0.04525 0.03475 0.05500 0.03767 0.04000 0.03600 0.04000 0.05700 0.04000
g(2005-2015) 0.05025 0.03975 0.06000 0.04267 0.04500 0.04100 0.04500 0.06200 0.04500
nital coAdidons (mniHon GDP 199S) o.0Oo00 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
CORS 5.03310 2.00000 2.69900 2.67200 3.20300 3.50000 3.16900 3.62600 2.00000
y 47,085.0 486.0 74,610.0 99,920.4 7,604 12358.6 38,387.0 22,600.0 4,880.0
C 34,123.4 440.3 57,607.4 81 ,708.9 6,023.6 9,639.7 29,110.6 16,583.6 3,972.6
I/Y 0.3 0.1 0.2 0.2 0.2 0.2 0.2 0.3 0.2
K 175,019.6 2,173.4 249,224.6 318,825.9 36,482.8 37,075.8 168,344.7 69,667.7 I1,256.4
A 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 c 0.0
N 16.0 0.3 34.3 34.4 3.1 1.7 15.1 5.2 7.6
IMPACTS OF THE EPIDEMIC _
Shre AIDS relted deads aniong skill [20%/6-50°j] [20%v-5O. [20%-50%° [20%-50%] [20ffo-50l] [20%-50%1 [20°/.-50%1 [20%-50°h [200-S0%1
Imwact labor productivity (dl ) [0-0.5] [0-0.51 [ [0-0.5] [0-0.5] [0-0.51 [0-0.51 [040.51 0.51
Impaa health spending (d2) 0 0 0 0 0 0 0 0 0
InitialcostofADS=treatment/GDP 1.5 1 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5
Marginal increase in the cost 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95
Access to teatnent 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
HIVWAIDS DIFFUSION
Enldendology
Share sex workers -0.01%-0.504 1.00% 0010l% -0.5% 0.1 '.%0.01%-.5 001%-0.5 0OI.01-0.5% 0.01-0.5%L0.01%0/w.5%
Share IDUS I [0.01°4.I% 0.00%h 0.01 1%/' [0.OI/0-0.1% 0.00%/ [.01Yo40.1% '0.01Y-0.1 0.01Y04.1°/O 0.00%/0
Share IDUs2 0.01-0 .1% 0.00%/ 'O.01 / % 0. I Y.01 /o.1% 0.00%/ 0.014-0.1% 0.O.1/0-0.1 0.IY.0.l% 0.00%/
Share infected sex workers 10.00°h 50.00/0 100°% 1.00°h 1.00%/0 1.00% 1.00% 1.00% 7.00°/
Share infected IDUsl 0.30°/ 0.00%h 1.00%/ 1.00%/ 0.00%/. 2.20% 0.34% 0.34% 0.00%h
Share infected IDUs2 0.30°h 0.00°h 1.00°h 1.00%° 0.00%h 2.20°/ 0.34% 0.34% 0.00%h
Aggeate prevalence rate 0.07% 6.00/. 0.02% 0.03% 0.02% .0.09% 0.03% 0.06% 0.02%
PrventlvetHealth Behavior .
Condom use [10%-50%[1 IY-50% [I 0%-50%0 [10[I-50] [I0%-50%l [fI0%-50°/I [10%-50°/ [100-50%]
Condom effectiveness 98.00%h 98.00%h 98.00%h 98.00%/ 98.00h/. 98.00/. 98.00% 98.00%/ 98.00%h
Needle sharing [10%-50 Yo 10%-S1O lOY.-50°i [I 0%-50Yo1 [f0%-50°h] [ 10°h-50%1 [pOY.-50/l1 [IOY-50%] [I OYo-50Y1o
Averge number of injections per year 730 730 730 730 730 730
STDs prevalence [0%-5%] [0%.-5%] [0%-5%1 [00%-5°1 [0Y-5%]1 [0%-5%] [0%1-5%1 [0Y.-5%]1 [0%-5%1l
- 27 -
Parameters determining the diffusion of the epidemic
Given the lack of epidemiological and behavioral data, we allow the different parameters to
vary uniformly along wide intervals. We start by defining the approximate BIIV/AIDS prevalence
rates in the general population and prevalence rates arnong high-risk groups (sex workers and IDUs).
For this we use UNAIDS country profiles and data collected for this study. In the case of Morocco
where data on the prevalence rate of IDUs are not available, we assume levels equivalent to Tunisia.
There are no reliable data regarding the population share of sex workers and IDUs in the different
countries. Thus, we treat these shares as exogenous parameters that are allowed to change between
simulations from a conservative baseline; changes range between 0 and 0.1%. Given the share of the
different population groups, the aggregate prevalence rate, and the prevalence rates for the high-risk
groups, we compute the implicit prevalence rates for the low-risk groups.
There are also scarce data regarding sex behaviors and drug use. The probability of using a
condom is allowed to vary between 10%0/ (close to Morocco) and 40% (close to Jordan). The
probability of sharing a needle is allowed to vary between 100/% and 50% while the average number of
injections per year is set at 730 (on the basis of Jenkins et al., 2001). Finally, the STDs prevalence is
allowed to vary between 0 and 5%. The total number of STDs cases is equally divided between non-
ulcerative, ulcerative, and both.
The second important set of parameters that affects the dynamics of the HIV/AIDS epidemic
is given by the level and heterogeneity of sexual activity. Table B2 presents the average number of
partners across population groups and the average number of sexual intercourses. The numbers are
based on Rehle et al. (1998). Given high uncertainty about the correct values across countries, we
also allow these parameters to vary (all in the same proportion). The matrices are thus multiplied in
different simulations by a scalar ranging between 1 and 2.
Table C2: Average number of partner and sexual intercourses per year
Partners Sex workers IDUs males IDUs females Low-risk M Low-risk F
Sex workers 0 5 0 30 0
IDUs males 5 0 5 0 1
IDUs females 0 5 0 2 0
Low-rlskM 0.8 0 0.016 0 2
Low-risk F 0 0.008 0 2 0
Contacts Sex workers IDUs males IDUs females Low-risk M Low-risk F
Sex workers 0 10 0 10 0
IDUs males 10 0 40 0 10
IDUs females 0 40 0 10 0
Low-risk M 5 0 10 0 50
Low-risk F 0 10 0 50 0
Source: Based on Rehle et al. (1998)
The final set of model parameters relates to HIV/AIDS transmission probabilities. As
previously discussed, we consider two mechanisms: sexual transmission and needle sharing. In the
case of sexual transmission we allow the probability of infection to vary as a function of the presence
- 28 -
of the 3 different types of STDs (ulcerative, non-ulcerative, and both). The different probabilities are
summarized in Table 3. Diffusion profiles for selected scenarios are presented in Appendix D.
Table C3: lIV/AIDS transmission probabilities
IDUs
Sex workers Males IDUs females Low-risk M Low-risk F
Sex workers 0 10 0 10 0
IDUs males 10 0 40 0 10
IDUs females 0 40 0 10 0
Low-risk M 5 0 10 0 50
Low-risk F 0 10 0 50 0
Source: Rehle et al. (1998)
- 29 -
Appendix D: Selected HIV/AIDS diffusion profiles
Panel A Panel B
°1- / -
Cu,~~~~~~~~Ya Y..,@
_ / o -. x,- X
2000 2004 2005 2012 2015 2020 202 2000 2004 2005 2012 2015 2020 2024
Yo., Yeao
Panel C Panel D
o 0
o2000 2004 2005e 22:2 2015 2020 2024 °2000 2004 2000 20:2 2015 2020 2024
Yeor Yeor
Parameters:__ _ _ ___ _ _ _ _ _ _ _ _
Probability Probability STDs Share high Prevalence Average Average
of using a of sharing a prevalence rsk in high risk number of number of
condom needle population population partners contacts
Panel A 0.455556 0.855556 0.005556 0.000111 0.005556 1.999222 1.222222
Panel C 0.277778 0.67T778 0.027778 0.000556 0.027778 2.111111 2.111111
Panel D 0.188889 0.588889 0.038889 0.000778 0.038889 2.555556 2.555556
Panel E 0.1 0.5 0.05 0.001 0.05 3 3
WdOO 6.0000) 0) wooo.ooooO0O 0) *010
0 .1 Ott 0 ,01 0 090l O, 0 OR 0 000 .1
I '~"'' '''I'1ll
I~ ~~~wo ,gil 111lP
USWSA
(000 6.0000,0) ~0 ,00000ti 600) WO0.0000)) ' -oooO00)- 560..)
WasO ..A0P.t X) o_ZodO > c (.00 .,A.P.1 %) .1 ...pO IU *l
00) Oi, 0o) 00) 01, 0 00c0 0000 00 0 00 01 051O, O, tt 001 00 0) 00 0000 00 000 0 01 Ot 0.
*'' II 111 111
III -1~~IT
(dOt 0.0000) 0) go_o rdSO. 00 1) 600) (000 6.00000 0) 6sz_o0 o r0dOS tl 000
Ost 0)., 00) 001 0,, 00 00 00 000 000 0 0 g 01 00 oc 0 0) 00I 0C. 0500 000 00 00 00 00 1OD OtO
uouuqa'l uspior
(000 0.0000) i) 0000.000000000 II) 0601OO (0006.000) . 0) 0. . Z _dOOa00 ) 0 -01
0' 1 O tt 0 0) 0 0) 0)) 0 0 00 00 00 P 00 00 0 Ot O OSt a01 001 01 0C ) 00 0 0 0 . 00 00 0 1
uw.q ~~~~~~~;daZ:E
(dOO 0.0000 0) 0000..o00rSO .,0*01 (dOO 4.0000) 0) -.A--I dO04 0 )01
0s,0) O CI 00) 00) 0)) 0 00 Ot 00 00 00 00 00 0) 001 0t) 00) 00) 0) 00, 00 00 00 00 00 0 0 00 O 0.
pnoqlfa epa2W
3jaU p!da SCW/AIH aql moij 2lU4 Isai (lSozooZjz) sassol Ja!) jo uotpfnqc4s4 :g a-inlig
onb-snqvs aq) ,apun salquspA amwono papaas .oj suopnqj4s!j :j xjpuaddV
- 0E -
- 31 -
Figure E2: Distribution of reductions in the GDP growth rate (2000-2025) resulting
from the IlV/AIDS epidemic
Algeria Djibouti
-tO -16 .t -'0 0. - 0oe -o o -e -. -, - . .
20-q0 g-0.0 Iot. of 02P 2000-2025 (5) 90o10 - W.otO 1cup ol CP 20050-2025 (0)
Egypt Iran
. . . ...lh.*IlIl - _ II. .. .111 *Iii! IIIL
-. 0 e 1 I - - 1.0 - 0. 0 -2.6 - . -.6 -1.o -o.. 0. 2
20. ogO 010W.1 .01 601 00 2000-2025 ( 0) 200005. gro._I .role 1 GOP 2000-2025 ( 0)
Jordan Lebanon
-. 1. -I. -1. 0.o o 0.2 -2.0 ° 2.v. .- 1 -,.o -o.e
W-troq -q o_I Goeo OP 2000-2023 x A-9.q qloI rO, GD CP 20DO-2D25 MX
Morocco Tunisia
-. - 1. 4 -I . . 0_ 2 -0.6 0 .- *1. . _.O -_0.6 -O0
g-t.oq 1 .0.ot, of GOP 2000-2025 (0) .r.log . q 0_Ih .01. of 00P 2000-2025 (0)
Yemen
...r.g. 9q., r_th .1 o GOP 2000-202* (X)
- 32 -
Figure E3: Distribution of reductions in the labor force in year 2025
Algeria Djlbouti
Popolotioo OlIn (2 *OdoolIO In 2025) o( ndOctlo In 2025)
Egypt Iran
,, 1 O . . . ,A,III III, - -
PoL,lotlo loo (X rooUoo I 2025) 2025)
Jordan Lebanon
-ro -is -6 -4 ~~-2 - -2 -sD _s T _ -2
P00001050 loon (0 ,.05otioo In 2025) P1ODUoIloon . (0 .oOlon In 2025)
Morocco Tunisia
Z ~ ~~ ~~ ~ ~~~~~~~~~~~~~~~~ 111 n !
P0iouIUoob lonses (0 ,.doal In 2025) POo..Wllon lo..- (0 .educ.0- In 2025)
Yemen
0000l01100 lon.l (0 r.iuctloO In, 2025)
- 33 -
Figure E4: Distribution of the HIV/AIDS prevalence rate in 2015
Algeria Djibouti
a 4 * * 10 o .i *ss s 0 20 . 50 40 00
0fl5/0105 0Dr00-nflc n 20's (0) I0IV/A4S Drlono In 2015 (M)
Egypt Iran
a B B 012 14 16 2 0 5 '0 10 - 6
-4-*10, plen5b I- 2015 (S) 0N,'AIDS proIl100 I 2Q12 tX)
Jordan Lebanon
0 'O S 50 ;, ,. . Ii IIikI1d O1 2 ..
. . a ,0 ., 0 . , . O 0 12 tO *.
0NVAIDS -5 ....Dr-lc In 2015 (0) 057*100 D.-oo.bo. h -j0S (0)
Yemen
Ul0/AI0t nr1v0 0 IIh 2015 (0I
- 34 -
Figure E5: Distribution of HIV/AIDS related health expenditures in year 2015
Algeria DJiboutl
8114'*l0 12101.0 000118 2.D 11 2015 (SGOP) 021/A005 1.10190 0.0110 @2 In 2015 (5009)
Egypt Irn
,i' - g,
/W^0lDs 1.10300 i020h *.p In 2015 (SGDP) UIV/0I05 .210120 0.0118 00 In 2015 (X00P)
Jordan Lebanon
../.-Os 1210t0d 0201P0 020 II, 2015 (8009) i 1 0120 eolil 200 In 0015 (XC0P)
Morocco Tunisia
2 0 5 2l 1010101 0I 2 * 2i 5 10 2 12 1
14'I t.OS d210a H012th 022 I.. 2015 (X00P) O I .W* 4 1 .10120 0.01h II2 In 2015 (X0OP)
Yemen
,I^0&'IS 1.10100 02011h -P In 2015 (XGOP)
Policy Research Working Paper Series
Contact
Title Author Date for paper
WPS2854 Rich andPowerful? Subjective Michael Lokshin June 2002 C. Cunanan
Power and Welfare in Russia Martin Ravallion 32301
WPS2855 Financial Crises, Financial Luc Laeven June 2002 R. Vo
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Randy Kroszner
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WPS2857 Markups, Returns to Scale, and Hiau Looi Kee June 2002 M. Kasilag
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Experience from Country Mierta Capaul 32623
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Disparities in Sri Lanka Pradeep Kurukulasuriya 84321
WPS2860 Privatization in Competitive Sectors: Sunita Kikeri June 2002 R. Bartolome
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WPS2861 Trade-Related Technology Diffusion Maurice Schiff June 2002 M. Kasilag
and the Dynamics of North-South Yanling Wang 39081
and South-South Integration Marcelo Olarreaga
WPS2862 Tenure, Diversity, and Commitment: Somik V. Lall June 2002 Y. D'Souza
Community Participation for Urban Uwe Deichmann 31449
Service Provision Mattias K. A. Lundberg
Nazmul Chaudhury
WPS2863 Getting Connected: Competition Frew Amare Gebreab June 2002 P. Sintim-Aboagye
and Diffusion in African Mobile 38526
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Uganda F. F. Tusubira 38526
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WPS2865 Bankruptcy Around the World: Stijn Claessens July 2002 A. Yaptenco
Explanations of its Relative Use Leora F. Klapper 31823
WPS2866 Transforming the Old into a David Ellerman July 2002 N. Jameson
Foundation for the New: Lessons Vladimir Kreacic 30677
of the Moldova ARIA Project
Policy Research Working Paper Series
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WPS2867 Cotton Sector Strategies in West Ousmane Badiane July 2002 A. Lodi
and Central Africa Dhaneshwar Ghura 34478
Louis Goreux
Paul Masson
WPS2868 Universal(ly Bad) Service: George R. G. Clarke July 2002 P. Sintim-Aboagye
Providing Infrastructure Services Scott J. Wallsten 38526
to Rural and Poor Urban Consumers
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A Complement to National/ Steven B. Webb
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In Financial Transactions-Public Tom Kellermann 85984
Policy Issues Valerie McNevin
WPS2871 Pricing of Deposit Insurance Luc Laeven July 2002 R. Vo
33722
WPS2872 Regional Cooperation, and the Role Maurice Schiff July 2002 P. Flewitt
of International Organizations and L. Alan Winters 32724
Regional Integration
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